Methods and systems for electric propulsor fault detection

ABSTRACT

Systems and methods relate to electric propulsor fault detection. An exemplary system includes at least a first inverter configured to accept a direct current and produce an alternating current, a first propulsor, a first motor operatively connected with the first propulsor and powered by the alternating current, and at least a noise monitoring circuit electrically connected with the direct current and configured to detect electromagnetic noise and disengage the at least an inverter as a function of the electromagnetic noise.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of Non-provisionalapplication Ser. No. 17/366,449 filed on Jul. 2, 2021, and entitled“METHODS AND SYSTEMS FOR ELECTRIC PROPULSOR FAULT DETECTION,” theentirety of which is incorporated herein by reference.

FIELD OF THE INVENTION

The present invention generally relates to the field of aircraftnavigation and controls. In particular, the present invention isdirected to methods and system for electric propulsor fault detection.

BACKGROUND

Presently electric multirotor aircraft have seen impressive advancement.However, commercial air travel is one of the safest modes of humantransportation. Electric multirotor aircraft must have a demonstratedtrack record for safety before the public will benefit from the use ofelectric multirotor aircraft.

SUMMARY OF THE DISCLOSURE

In an aspect an electric propulsor fault detection system includes atleast a first inverter configured to accept a direct current and power afirst motor, a first propulsor, wherein the first propulsor is mountedto an electric aircraft and configured to produce lift, the first motoroperatively connected with the first propulsor and at least a noisemonitoring circuit electrically connected with the direct current andconfigured to detect noise having a frequency within a specified rangeand disengage the at least an inverter as a function of the detectednoise.

In another aspect a method of electric propulsor fault detectionincludes accepting, using at least a first inverter, a direct current,powering, using the at least a first inverter, a first motor operativelyconnected with a first propulsor, wherein the first propulsor is mountedto an electric aircraft and configured to produce lift, detecting, usingat least a noise monitoring circuit electrically connected with thedirect current, noise having a frequency within a specified range, anddisengage, using the at least a noise monitoring circuit, the at leastan inverter as a function of the detected noise.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is a block diagram illustrating an exemplary electric propulsorfault detection system;

FIG. 2 is a block diagram of exemplary electric propulsor faultdetection systems with a plurality of motors;

FIG. 3 is a schematic of an exemplary electric propulsor fault detectionsystem;

FIG. 4 is a graph illustrating exemplary electromagnetic noises;

FIG. 5 is an illustration of an exemplary electric aircraft;

FIG. 6 is a block diagram of an exemplary flight controller;

FIG. 7 is a block diagram illustrating exemplary machine-learningprocesses;

FIG. 8 is a flow diagram of an exemplary method of electric propulsorfault detection; and

FIG. 9 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for electric propulsor fault detection, for exampleon an electric aircraft. In an embodiment, electromagnetic noise resultsfrom a fault of a propulsor or corresponding motor or inverter.Electromagnetic noise may further disrupt other flight systems,including without limitation flight controllers, actuators, flightcomponents, and/or other propulsors or motors.

Aspects of the present disclosure can be used to detect a faultcorresponding with electromagnetic noise. Aspects of the presentdisclosure can also be used to disconnect (from an electrical energysource) or otherwise disengage a flight component experiencing adetected fault. This is so, at least in part, because, in some case,noise monitoring circuit responsible for detecting a fault may beelectrically connected to power lines supplying a faulty flightcomponent and therefore able to detect not only a fault, but whichflight component is experiencing the fault. Exemplary embodimentsillustrating aspects of the present disclosure are described below inthe context of several specific examples.

Referring now to FIG. 1 , an exemplary embodiment of an electricpropulsor fault detection system 100 is illustrated. System includes anoise monitoring circuit 104. As used in this disclosure, a “noisemonitoring circuit” is an electrical circuit configured to detectelectromagnetic noise. As used in this disclosure, “electromagneticnoise” is an electromagnetic and/or radiofrequency disturbance affectingan electric circuit. Electromagnetic noise may include electromagneticinterference and/or radiofrequency interference. Electromagnetic noisemay originate from a source external to electric circuit; alternativelyor additionally, electromagnetic noise may originate from one or morecomponents within electrical circuit. In some cases, electromagneticnoise may include differential mode current. Alternatively oradditionally, in some cases, electromagnetic noise may include commonmode current. As used in this disclosure, “common mode current” is aflow of an electric charge having a path that include a common (e.g.,ground). In some cases, a common mode current path may includecapacitance (e.g., capacitor, air-gap between conductors, and the like)substantially upstream from common. As used in this disclosure, a“differential mode current” is a flow of an electrical charge having aflow path constituting that of an electrical circuit. For example, adifferential mode current may include a flow of electrical charge havinga flow path than includes a supply conductor and a return conductor ofan electrical circuit.

With continued reference to FIG. 1 , noise monitoring circuit 104 mayinclude any computing device as described in this disclosure, includingwithout limitation a microcontroller, microprocessor, digital signalprocessor (DSP) and/or system on a chip (SoC) as described in thisdisclosure. Computing device may include, be included in, and/orcommunicate with a mobile device such as a mobile telephone orsmartphone. Noise monitoring circuit 104 may include a single computingdevice operating independently, or may include two or more computingdevice operating in concert, in parallel, sequentially or the like; twoor more computing devices may be included together in a single computingdevice or in two or more computing devices. Noise monitoring circuit 104may interface or communicate with one or more additional devices asdescribed below in further detail via a network interface device.Network interface device may be utilized for connecting noise monitoringcircuit 104 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Noisemonitoring circuit 104 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Noise monitoring circuit 104 may include one or more computingdevices dedicated to data storage, security, distribution of traffic forload balancing, and the like. Noise monitoring circuit 104 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Noisemonitoring circuit 104 may be implemented using a “shared nothing”architecture in which data is cached at the worker, in an embodiment,this may enable scalability of system 100 and/or computing device.

With continued reference to FIG. 1 , noise monitoring circuit 104 may bedesigned and/or configured to perform any method, method step, orsequence of method steps in any embodiment described in this disclosure,in any order and with any degree of repetition. For instance, noisemonitoring circuit 104 may be configured to perform a single step orsequence repeatedly until a desired or commanded outcome is achieved;repetition of a step or a sequence of steps may be performed iterativelyand/or recursively using outputs of previous repetitions as inputs tosubsequent repetitions, aggregating inputs and/or outputs of repetitionsto produce an aggregate result, reduction or decrement of one or morevariables such as global variables, and/or division of a largerprocessing task into a set of iteratively addressed smaller processingtasks. Noise monitoring circuit 104 may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing. In some cases, noise monitoring circuit 104may not include a computing device, for example in some cases, the noisemonitoring circuit 104 may include a hardwired electrical circuit, forexample a circuit as described in reference to FIGS. 2-3 .

With continued reference to FIG. 1 , system 100 may include an energysource 108. Energy source 108 may include an electrochemical cell, forinstance a battery. Energy source 108 may include any battery describedin this disclosure, including with reference to FIG. 5 . Energy source108 may be configured to provide a direct current. As used in thisdisclosure, “direct current” is one directional flow of electric charge.Direct current may be provided by way of one or more electrochemicalcells (e.g., batteries). In some cases, noise monitoring circuit 104 maybe connected in-line with one or more conductive lines carrying directcurrent from energy source 108. For example, in some cases, noisemonitoring circuit 104 may electrically connected with direct currentfrom energy source 108.

With continued reference to FIG. 1 , system 100 may include an inverter112. An “inverter,” as used in this this disclosure, is a powerelectronic device or circuitry that changes direct current (DC) toalternating current (AC). An inverter (also called a power inverter) maybe entirely electronic or may include at least a mechanism (such as arotary apparatus) and electronic circuitry. In some embodiments, staticinverters may not use moving parts in conversion process. Inverters maynot produce any power itself; rather, inverters may convert powerproduced by a DC power source. Inverters may often be used in electricalpower applications where high currents and voltages are present;circuits that perform a similar function, as inverters, for electronicsignals, having relatively low currents and potentials, may be referredto as oscillators. In some cases, circuits that perform oppositefunction to an inverter, converting AC to DC, may be referred to asrectifiers. Further description related to inverters and their use withelectrical motors used on electric VTOL aircraft is disclosed withinU.S. patent applications Ser. Nos. 17/144,304 and 17/197,427 entitled“METHODS AND SYSTEMS FOR A FRACTIONAL CONCENTRATED STATOR CONFIGURED FORUSE IN ELECTRIC AIRCRAFT MOTOR” and “SYSTEM AND METHOD FOR FLIGHTCONTROL IN ELECTRIC AIRCRAFT” by C. Lin et al. T. Richter et al.,respectively, both of which are incorporated herein by reference intheir entirety. Inverter 112 may be configured to accept direct currentand produce alternating current. As used in this disclosure,“alternating current” is a flow of electric charge that periodicallyreverses direction. In some cases, an alternating current maycontinuously change magnitude overtime; this is in contrast to what maybe called a pulsed direct current. Alternatively or additionally, insome cases an alternating current may not continuously vary with time,but instead exhibit a less smooth temporal form. For example, exemplarynon-limiting AC waveforms may include a square wave, a triangular wave(i.e., sawtooth), a modifier sine wave, a pulsed sine wave, a pulsewidth modulated wave, and/or a sine wave. In some cases, noisemonitoring circuit may electrically connect to direct current which isprovided to inverter 112.

With continued reference to FIG. 1 , system 100 may include a motor 116.Motor may include any motor described in this disclosure, including withreference to FIG. 5 . Motor 116 may be electrically connected toinverter 112. Motor 116 may be powered by alternating current producedby inverter 112. Motor 116 may be operatively connected with a propulsor120. Propulsor may include any propulsor described in this disclosure,including with reference to FIG. 5 . Motor may operate to move one ormore flight control components and/or one or more control surfaces, todrive one or more propulsors, or the like. A motor may be driven bydirect current (DC) electric power and may include, without limitation,brushless DC electric motors, switched reluctance motors, inductionmotors, or any combination thereof. Alternatively or additionally, amotor may be driven by an inverter 112. A motor may also includeelectronic speed controllers, inverters, or other components forregulating motor speed, rotation direction, and/or dynamic braking.

With continued reference to FIG. 1 , noise monitoring circuit 104 may beconfigured to detect electromagnetic noise and disengage an inverter 112as a function of the electromagnetic noise. As used in this disclosure,“disengaging” an electrical component, for example, an inverter preventsthe function of the electrical component. Non-limiting examples ofdisengagement of an electrical inverter 112 include disconnecting theinverter 112 from an energy source 108, disconnecting the inverter 112from a motor 116, or disabling function of the inverter 116. In somecases, an inverter 116 may be disengaged by prevent switching of currentwithin inverter. In some embodiments, noise monitoring circuit 104 mayinclude a filter. As used in this disclosure, a “filter” is a circuitthat is configured to selectively block and/or pass certain signals. Anexemplary filter is a bandpass filter than selectively blocks and/orpasses frequencies within a certain range, i.e., band. In some cases,noise monitoring circuit 104 may include an electromagnetic interference(EMI) filter. As used in this disclosure, an “electromagneticinterference filter” is an electrical circuit or device that mitigateselectromagnetic noise present on an electrical conductor or circuit. Insome cases, an electromagnetic interference (EMI) filter may be active,passive, or both. As used in this disclosure, an electrical circuit is“passive” when it comprises substantially all passive components. Asused in this disclosure, a “passive component” is an electricalcomponent that requires no additional electrical power to operate.Non-limiting examples of passive components include inductors,capacitors, transformers, and resistors. Alternatively, as used in thisdisclosure, an electrical circuit is “active” when is comprises at leastone (necessary) active component. As used in this disclosure, an “activecomponent” is an electrical component that provides and/or switcheselectrical energy. An active component may require additional electricalpower to operate. Non-limiting examples of active electrical componentsinclude voltage sources, current sources, transistors, digitalcontrollers, computing devices, and the like.

Still referring to FIG. 1 , in some embodiments, noise monitoringcircuit 104 may include a noise detection signal connected with EMIfilter. As used in this disclosure, a “noise detecting signal” is atleast a signal that indicates electromagnetic noise on an electricalcircuit. In some cases, noise detection signal may be inductivelyconnected with EMI filter, for example as is described with reference toFIG. 3 . Noise detecting signal may include any type of signal. In somecases, noise monitoring circuit 104 may perform one or more signalprocessing steps on noise detecting signal. For instance, noisemonitoring circuit 104 may analyze, modify, and/or synthesize noisedetecting signal in order to improve the signal, for instance byimproving transmission, storage efficiency, or signal to noise ratio.Exemplary methods of signal processing may include analog, continuoustime, discrete, digital, nonlinear, and statistical. Analog signalprocessing may be performed on non-digitized or analog signals.Exemplary analog processes may include passive filters, active filters,additive mixers, integrators, delay lines, compandors, multipliers,voltage-controlled filters, voltage-controlled oscillators, andphase-locked loops. Continuous-time signal processing may be used, insome cases, to process signals which vary continuously within a domain,for instance time. Exemplary non-limiting continuous time processes mayinclude time domain processing, frequency domain processing (e.g.,Fourier transform), and complex frequency domain processing. Discretetime signal processing may be used when a signal is samplednon-continuously or at discrete time intervals (i.e., quantized intime). Analog discrete-time signal processing may process a signal usingthe following exemplary circuits sample and hold circuits, analogtime-division multiplexers, analog delay lines, and analog feedbackshift registers. Digital signal processing may be used to processdigitized discrete-time sampled signals. Commonly, digital signalprocessing may be performed by a computing device or other specializeddigital circuits, such as without limitation an application specificintegrated circuit (ASIC), a field-programmable gate array (FPGA), or aspecialized digital signal processor (DSP). Digital signal processingmay be used to perform any combination of typical arithmeticaloperations, including fixed-point, floating-point, real-valued andcomplex-valued, multiplication and addition. Digital signal processingmay additionally operate circular buffers and lookup tables. Furthernon-limiting examples of algorithms that may be performed according todigital signal processing techniques include fast Fourier transform(FFT), finite impulse response (FIR) filter, infinite impulse response(IIR) filter, and adaptive filters such as the Wiener and Kalmanfilters. Statistical signal processing may be used to process a signalas a random function (i.e., a stochastic process), utilizing statisticalproperties. For instance, in some embodiments, a signal may be modeledwith a probability distribution indicating noise, which then may be usedto reduce noise in a processed signal.

Still referring to FIG. 1 , in some embodiments, electromagnetic noisemay include a common mode noise; and EMI filter may include a commonmode filter. As used in this disclosure, a “common mode noise” is anelectromagnetic or radiofrequency disturbance of a common mode current.As used in this disclosure, a “common mode filter” is an electromagneticfilter that acts upon common mode noise.

Still referring to FIG. 1 , in some embodiments, electromagnetic noisemay include a differential mode noise; and EMI filter may include adifferential mode filter. As used in this disclosure, a “differentialmode noise” is an electromagnetic or radiofrequency disturbance of adifferential mode current. As used in this disclosure, a “differentialmode filter” is an electromagnetic filter that acts upon differentialmode noise.

Still referring to FIG. 1 , in some embodiments, electromagnetic noisemay be indicative of fault. As used in this disclosure, a “fault” is anoccurrence that is outside of specified bounds. For example, a fault mayoccur when a motor operates outside of normal specified operatingconditions. A fault may also occur when a motor fails to operate at all.Non-limiting examples of faults include motor neutral ground faults,motor phase ground faults, motor winding ground fault, turn to turnfault, inverter DC bus ground fault, battery bus ground fault. In somecases, fault may include a motor fault. As used in this disclosure, a“motor fault” is a fault involving a motor, for example when a motor 116operates outside a specified boundary. In some cases, fault may includean inverter fault. As used in this disclosure, a “inverter fault” is afault involving an inverter, for example when an inverter 112 operatesoutside a specified boundary.

Still referring to FIG. 1 , in some embodiments, propulsor 120 may bemounted to an electric aircraft and configured to produce lift. In somecases, electric aircraft may additionally include a second inverter, asecond motor powered by the second inverter, a second propulsoroperatively connected to the second motor, mounted to the electricaircraft, and configured to produce lift. In some cases, electromagneticnoise may disturb at least one of second inverter, second motor, andsecond propulsor. Disclosure related to a plurality of inverters andmotors is described below with reference to FIG. 2 .

Referring now to FIG. 2 , a schematic is shown comprising a plurality ofelectrical propulsor fault detection systems 200. From left to right,plurality of systems 200 may include energy sources 204 a-b, switches208 a-b, electromagnetic filters 212 a-b with connectors carrying noisedetecting signals 216 a-b, inverters 220 a-b, and motors 224 a-b. Insome embodiments, a fault and resulting electromagnetic noise from oneor more of a first motor 224 a and/or a first inverter 220 a may disruptperformance of one or more of second motor 224 b and/or second inverter220 b. Therefore, in some cases, fault may be detected, using a noisemonitoring circuit electrically connected to a first noise detectingsignal 216 a, and presence of the fault may be used to disengage (e.g.,disconnect power to) first inverter 220 a, for example by way of one orswitches 208 a-b. Alternatively or additionally, in some cases, firstinverter 220 a may be disengaged without being disconnected. In somecases, first inverter 220 a may switch current internally in order toproduce alternating current; and first inverter 220 a may be disengagedinternally by disabling switching of current within first inverter 220a. In some cases, first inverter 220 a may be disengaged bydisconnecting alternating current from first inverter 220 a to firstmotor 224 a or otherwise removing an electrical load from the firstinverter 220 a. In some cases, a switch and/or relay may be used toselectively disconnect power to one or more inverters 220 a-b. Exemplarynon-limiting relays include coaxial relays, contactor relays,force-guided contacts relay, latching relays, machine tool relays,mercury relays, mercury-wetted relays, multi-voltage relays, overloadprotection relays, polarized relays, Reed relays, safety relays,solid-state contactor relays, solid state relays, static relays timedelay relays, vacuum relays, and the like. In some cases, system 100,200 may be designed and configured to operate (and selectively switch)high-potential, high-current, and/or high-power electricity. Forinstance in some cases, system 100, 200 may be designed to deliver atleast 3KV of potential from energy sources 204 a-b to inverters 220 a-b.Motors 224 a-b may be operatively coupled and configured to power anyflight component, described in this disclosure, including for examplepropulsory (e.g., lift propulsory).

Referring now to FIG. 3 , a schematic illustrates an exemplary noisemonitoring circuit 300. In some embodiments, noise monitoring circuit300 may be located on a direct current side of an inverter, for exampleas described above in reference to FIGS. 1-2 . Direct current powerlines are illustrated within circuit 300 running right to left withinputs 304 a-b and outputs 308 a-b. Circuit 300 may include anelectromagnetic interference (EMI) filter 312. EMI filter 312 mayinclude a common mode noise filter and/or a differential mode noisefilter. EMI filter 312 may include one or more of resistance-capacitance(RC) circuits, resistance-inductance (RL) circuits,inductance-capacitance (LC) circuits and/orresistance-inductance-capacitance (RLC) circuits, for example as one ormore of a common mode noise filter and/or a differential mode noisefilter. Exemplary EMI filter 312 schematically illustrated in FIG. 3 ,may have passive electrical component parameters according to tablebelow:

Component Parameter Resistor 1, 2 (R1, R2) 10 KOhm Resistor 3, 4 (R3,R4) 10 Ohm Capacitor 1 (C1) 500 nF Capacitor 2, 3, 4 (C2, C3, C4) 50 nFCapacitor 5, 6, (C5, C6) 5 nF Capacitor 7, 8 (C7, C8) 200 nF Inductor 1,2 (L1, L2) 10 μH

Still referring to FIG. 3 , in some embodiments EMI filter 312 may beused to selectively block and/or pass electromagnetic noise havingcertain frequencies. For example, an EMI filter that includes an LCfilter may have a cutoff frequency approximately equal to:

$f_{c} = \frac{1}{2\pi\sqrt{LC}}$where, f_(c) is cutoff frequency (e.g., in Hertz), L is inductance(e.g., in Henry), and C is capacitance (e.g., in Farad). In someembodiments, impedance of EMI filter 312 may be considered. Forinstance, in order to avoid instability, such as without limitationMiddlebrook instability, impedance of EMI filter 312 may need to be muchlower than impedance of other components within circuit, such as withoutlimitation energy sources. In some embodiments, impedance of an LCcircuit may be estimated thus:

$Z_{F} = \sqrt{\frac{L}{C}}$where, Z_(F) is impedance of LC circuit.

With continued reference to FIG. 3 , in some embodiments, noisemonitoring circuit 300 may include a sub-circuit 316 for detecting anoise detecting signal. Sub-circuit 316 may be inductively coupled toEMI circuit 312, thereby allowing electromagnetic noise to becommunicated with the sub-circuit 316. In some cases, sub-circuit mayperform one or more signal processing or signal analyzing functions onnoise detecting signal. For example, sub-circuit 316 may perform anysignal processing and/or signal analysis functions described in thisdisclosure. In some cases, sub-circuit 316 may be connected, forinstance by way of one or more monitoring outputs 320 a-b, to acomputing device. For instance, monitoring outputs 320 a-b may connect anoise detecting signal to digital to analog converter, which maydigitize the noise detecting signal, thereby allowing a computing deviceto digitally analyze and/or process the noise detecting signal usingdigital methods. In some cases, computing device may selectively switchpower to one or more inverters as a function of noise detecting signal.

Referring now to FIG. 4 , an exemplary composite graph 400 is shown thatillustrates voltages over time for a number of representative faults. Asignal trace 404 represents an exemplary noise detecting signal. signaltrace 404 is illustrated with potential in volts along a vertical axisand time in milliseconds along a horizontal axis. Input potential isillustrated by a positive input trace 408 a and a negative input trace408 b. Output potential is illustrated by a positive output trace 412 aand a negative output trace 412 b. Both input potential 408 a-b andoutput potential 412 a-b are illustrated in graph 400 with potential inkilovolts represented along a vertical axis and time in millisecondsrepresented along a horizontal axis. Along horizontal (time) axis anumber of faults have been demonstrated. From graph 400, it can be seenthat faults can cause disruption to supply direct current, through largevacillations of input traces 408 a-b and output traces 412 a-b. Thisdisruption to direct current, if allowed to persist may disrupt otherelectronic components, such as without limitation electric propulsors.It can also be seen from graph 400 that faults may be detected throughregular excursions of signal trace 404. In some embodiments, system mayperform analysis on signal trace, including but not limited to amplitudethresholding, integrating, averaging, summing, and the like, todetermine presence and/or type of fault present. In some cases, systemmay selectively disconnect at least an inverter from direct current as aresult of analysis of signal trace.

Referring now to FIG. 5 , an exemplary embodiment of an aircraft 500 isillustrated. Aircraft 500 may include an electrically powered aircraft(i.e., electric aircraft). In some embodiments, electrically poweredaircraft may be an electric vertical takeoff and landing (eVTOL)aircraft. Electric aircraft may be capable of rotor-based cruisingflight, rotor-based takeoff, rotor-based landing, fixed-wing cruisingflight, airplane-style takeoff, airplane-style landing, and/or anycombination thereof. “Rotor-based flight,” as described in thisdisclosure, is where the aircraft generated lift and propulsion by wayof one or more powered rotors coupled with an engine, such as aquadcopter, multi-rotor helicopter, or other vehicle that maintains itslift primarily using downward thrusting propulsors. “Fixed-wing flight,”as described in this disclosure, is where the aircraft is capable offlight using wings and/or foils that generate lift caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

Still referring to FIG. 5 , aircraft 500 may include a fuselage 504. Asused in this disclosure a “fuselage” is the main body of an aircraft, orin other words, the entirety of the aircraft except for the cockpit,nose, wings, empennage, nacelles, any and all control surfaces, andgenerally contains an aircraft's payload. Fuselage 504 may comprisestructural elements that physically support the shape and structure ofan aircraft. Structural elements may take a plurality of forms, alone orin combination with other types. Structural elements may vary dependingon the construction type of aircraft and specifically, the fuselage.Fuselage 504 may comprise a truss structure. A truss structure may beused with a lightweight aircraft and may include welded aluminum tubetrusses. A truss, as used herein, is an assembly of beams that create arigid structure, often in combinations of triangles to createthree-dimensional shapes. A truss structure may alternatively comprisetitanium construction in place of aluminum tubes, or a combinationthereof. In some embodiments, structural elements may comprise aluminumtubes and/or titanium beams. In an embodiment, and without limitation,structural elements may include an aircraft skin. Aircraft skin may belayered over the body shape constructed by trusses. Aircraft skin maycomprise a plurality of materials such as aluminum, fiberglass, and/orcarbon fiber, the latter of which will be addressed in greater detaillater in this paper.

Still referring to FIG. 5 , aircraft 500 may include a plurality ofactuators 508. Actuator 508 may include any motor and/or propulsordescribed in this disclosure, for instance in reference to FIGS. 1-4 .In an embodiment, actuator 508 may be mechanically coupled to anaircraft. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, Hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. As used inthis disclosure an “aircraft” is vehicle that may fly. As a non-limitingexample, aircraft may include airplanes, helicopters, airships, blimps,gliders, paramotors, and the like thereof. In an embodiment, mechanicalcoupling may be used to connect the ends of adjacent parts and/orobjects of an electric aircraft. Further, in an embodiment, mechanicalcoupling may be used to join two pieces of rotating electric aircraftcomponents.

With continued reference to FIG. 5 , a plurality of actuators 508 may beconfigured to produce a torque. As used in this disclosure a “torque” isa measure of force that causes an object to rotate about an axis in adirection. For example, and without limitation, torque may rotate anaileron and/or rudder to generate a force that may adjust and/or affectaltitude, airspeed velocity, groundspeed velocity, direction duringflight, and/or thrust. For example, plurality of actuators 508 mayinclude a component used to produce a torque that affects aircrafts'roll and pitch, such as without limitation one or more ailerons. An“aileron,” as used in this disclosure, is a hinged surface which formpart of the trailing edge of a wing in a fixed wing aircraft, and whichmay be moved via mechanical means such as without limitationservomotors, mechanical linkages, or the like. As a further example,plurality of actuators 508 may include a rudder, which may include,without limitation, a segmented rudder that produces a torque about avertical axis. Additionally or alternatively, plurality of actuators 508may include other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjustmovement of aircraft 500. Plurality of actuators 408 may include one ormore rotors, turbines, ducted fans, paddle wheels, and/or othercomponents configured to propel a vehicle through a fluid mediumincluding, but not limited to air.

Still referring to FIG. 5 , plurality of actuators 408 may include atleast a propulsor component. As used in this disclosure a “propulsorcomponent” or “propulsor” is a component and/or device used to propel acraft by exerting force on a fluid medium, which may include a gaseousmedium such as air or a liquid medium such as water. In an embodiment,when a propulsor twists and pulls air behind it, it may, at the sametime, push an aircraft forward with an amount of force and/or thrust.More air pulled behind an aircraft results in greater thrust with whichthe aircraft is pushed forward. Propulsor component may include anydevice or component that consumes electrical power on demand to propelan electric aircraft in a direction or other vehicle while on ground orin-flight. In an embodiment, propulsor component may include a pullercomponent. As used in this disclosure a “puller component” is acomponent that pulls and/or tows an aircraft through a medium. As anon-limiting example, puller component may include a flight componentsuch as a puller propeller, a puller motor, a puller propulsor, and thelike. Additionally, or alternatively, puller component may include aplurality of puller flight components. In another embodiment, propulsorcomponent may include a pusher component. As used in this disclosure a“pusher component” is a component that pushes and/or thrusts an aircraftthrough a medium. As a non-limiting example, pusher component mayinclude a pusher component such as a pusher propeller, a pusher motor, apusher propulsor, and the like. Additionally, or alternatively, pusherflight component may include a plurality of pusher flight components.

In another embodiment, and still referring to FIG. 5 , propulsor mayinclude a propeller, a blade, or any combination of the two. A propellermay function to convert rotary motion from an engine or other powersource into a swirling slipstream which may push the propeller forwardsor backwards. Propulsor may include a rotating power-driven hub, towhich several radial airfoil-section blades may be attached, such thatan entire whole assembly rotates about a longitudinal axis. As anon-limiting example, blade pitch of propellers may be fixed at a fixedangle, manually variable to a few set positions, automatically variable(e.g. a “constant-speed” type), and/or any combination thereof asdescribed further in this disclosure. As used in this disclosure a“fixed angle” is an angle that is secured and/or substantially unmovablefrom an attachment point. For example, and without limitation, a fixedangle may be an angle of 2.2° inward and/or 1.7° forward. As a furthernon-limiting example, a fixed angle may be an angle of 3.6° outwardand/or 2.7° backward. In an embodiment, propellers for an aircraft maybe designed to be fixed to their hub at an angle similar to the threadon a screw makes an angle to the shaft; this angle may be referred to asa pitch or pitch angle which may determine a speed of forward movementas the blade rotates. Additionally or alternatively, propulsor componentmay be configured having a variable pitch angle. As used in thisdisclosure a “variable pitch angle” is an angle that may be moved and/orrotated. For example, and without limitation, propulsor component may beangled at a first angle of 3.3° inward, wherein propulsor component maybe rotated and/or shifted to a second angle of 1.7° outward.

Still referring to FIG. 5 , propulsor may include a thrust element whichmay be integrated into the propulsor. Thrust element may include,without limitation, a device using moving or rotating foils, such as oneor more rotors, an airscrew or propeller, a set of airscrews orpropellers such as contra-rotating propellers, a moving or flappingwing, or the like. Further, a thrust element, for example, can includewithout limitation a marine propeller or screw, an impeller, a turbine,a pump-jet, a paddle or paddle-based device, or the like.

With continued reference to FIG. 5 , plurality of actuators 508 mayinclude power sources, control links to one or more elements, fuses,and/or mechanical couplings used to drive and/or control any otherflight component.

Still referring to FIG. 5 , plurality of actuators 508 may include anenergy source. An energy source may include, for example, a generator, aphotovoltaic device, a fuel cell such as a hydrogen fuel cell, directmethanol fuel cell, and/or solid oxide fuel cell, an electric energystorage device (e.g. a capacitor, an inductor, and/or a battery). Anenergy source may also include a battery cell, or a plurality of batterycells connected in series into a module and each module connected inseries or in parallel with other modules. Configuration of an energysource containing connected modules may be designed to meet an energy orpower requirement and may be designed to fit within a designatedfootprint in an electric aircraft in which system may be incorporated.

In an embodiment, and still referring to FIG. 5 , an energy source maybe used to provide a steady supply of electrical power to a load over aflight by an electric aircraft 500. For example, energy source may becapable of providing sufficient power for “cruising” and otherrelatively low-energy phases of flight. An energy source may also becapable of providing electrical power for some higher-power phases offlight as well, particularly when the energy source is at a high SOC, asmay be the case for instance during takeoff. In an embodiment, energysource may include an emergency power unit which may be capable ofproviding sufficient electrical power for auxiliary loads includingwithout limitation, lighting, navigation, communications, de-icing,steering or other systems requiring power or energy. Further, energysource may be capable of providing sufficient power for controlleddescent and landing protocols, including, without limitation, hoveringdescent or runway landing. As used herein the energy source may havehigh power density where electrical power an energy source can usefullyproduce per unit of volume and/or mass is relatively high. As used inthis disclosure, “electrical power” is a rate of electrical energy perunit time. An energy source may include a device for which power thatmay be produced per unit of volume and/or mass has been optimized, forinstance at an expense of maximal total specific energy density or powercapacity. Non-limiting examples of items that may be used as at least anenergy source include batteries used for starting applications includingLi ion batteries which may include NCA, NMC, Lithium iron phosphate(LiFePO4) and Lithium Manganese Oxide (LMO) batteries, which may bemixed with another cathode chemistry to provide more specific power ifthe application requires Li metal batteries, which have a lithium metalanode that provides high power on demand, Li ion batteries that have asilicon or titanite anode, energy source may be used, in an embodiment,to provide electrical power to an electric aircraft or drone, such as anelectric aircraft vehicle, during moments requiring high rates of poweroutput, including without limitation takeoff, landing, thermal de-icingand situations requiring greater power output for reasons of stability,such as high turbulence situations, as described in further detailbelow. A battery may include, without limitation a battery using nickelbased chemistries such as nickel cadmium or nickel metal hydride, abattery using lithium ion battery chemistries such as a nickel cobaltaluminum (NCA), nickel manganese cobalt (NMC), lithium iron phosphate(LiFePO4), lithium cobalt oxide (LCO), and/or lithium manganese oxide(LMO), a battery using lithium polymer technology, lead-based batteriessuch as without limitation lead acid batteries, metal-air batteries, orany other suitable battery. Persons skilled in the art, upon reviewingthe entirety of this disclosure, will be aware of various devices ofcomponents that may be used as an energy source.

Still referring to FIG. 5 , an energy source may include a plurality ofenergy sources, referred to herein as a module of energy sources. Modulemay include batteries connected in parallel or in series or a pluralityof modules connected either in series or in parallel designed to satisfyboth power and energy requirements. Connecting batteries in series mayincrease a potential of at least an energy source which may provide morepower on demand. High potential batteries may require cell matching whenhigh peak load is needed. As more cells are connected in strings, theremay exist a possibility of one cell failing which may increaseresistance in module and reduce overall power output as voltage of themodule may decrease as a result of that failing cell. Connectingbatteries in parallel may increase total current capacity by decreasingtotal resistance, and it also may increase overall amp-hour capacity.Overall energy and power outputs of at least an energy source may bebased on individual battery cell performance or an extrapolation basedon a measurement of at least an electrical parameter. In an embodimentwhere energy source includes a plurality of battery cells, overall poweroutput capacity may be dependent on electrical parameters of eachindividual cell. If one cell experiences high self-discharge duringdemand, power drawn from at least an energy source may be decreased toavoid damage to a weakest cell. Energy source may further include,without limitation, wiring, conduit, housing, cooling system and batterymanagement system. Persons skilled in the art will be aware, afterreviewing the entirety of this disclosure, of many different componentsof an energy source. Exemplary energy sources are disclosed in detail inU.S. patent application Ser. Nos. 16/948,157 and 16/048,140 bothentitled “SYSTEM AND METHOD FOR HIGH ENERGY DENSITY BATTERY MODULE” byS. Donovan et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 5 , according to some embodiments, an energysource may include an emergency power unit (EPU) (i.e., auxiliary powerunit). As used in this disclosure an “emergency power unit” is an energysource as described herein that is configured to power an essentialsystem for a critical function in an emergency, for instance withoutlimitation when another energy source has failed, is depleted, or isotherwise unavailable. Exemplary non-limiting essential systems includenavigation systems, such as MFD, GPS, VOR receiver or directional gyro,and other essential flight components, such as propulsors.

Still referring to FIG. 5 , another exemplary actuator may includelanding gear. Landing gear may be used for take-off and/or landing/Landing gear may be used to contact ground while aircraft 500 is not inflight. Exemplary landing gear is disclosed in detail in U.S. patentapplication Ser. No. 17/196,719 entitled “SYSTEM FOR ROLLING LANDINGGEAR” by R. Griffin et al., which is incorporated in its entirety hereinby reference.

Still referring to FIG. 5 , aircraft 500 may include a pilot control512, including without limitation, a hover control, a thrust control, aninceptor stick, a cyclic, and/or a collective control. As used in thisdisclosure a “collective control” or “collective” is a mechanicalcontrol of an aircraft that allows a pilot to adjust and/or control thepitch angle of the plurality of actuators 508. For example and withoutlimitation, collective control may alter and/or adjust the pitch angleof all of the main rotor blades collectively. For example, and withoutlimitation pilot control 512 may include a yoke control. As used in thisdisclosure a “yoke control” is a mechanical control of an aircraft tocontrol the pitch and/or roll. For example and without limitation, yokecontrol may alter and/or adjust the roll angle of aircraft 500 as afunction of controlling and/or maneuvering ailerons. In an embodiment,pilot control 512 may include one or more foot-brakes, control sticks,pedals, throttle levels, and the like thereof. In another embodiment,and without limitation, pilot control 512 may be configured to control aprincipal axis of the aircraft. As used in this disclosure a “principalaxis” is an axis in a body representing one three dimensionalorientations. For example, and without limitation, principal axis ormore yaw, pitch, and/or roll axis. Principal axis may include a yawaxis. As used in this disclosure a “yaw axis” is an axis that isdirected towards the bottom of the aircraft, perpendicular to the wings.For example, and without limitation, a positive yawing motion mayinclude adjusting and/or shifting the nose of aircraft 500 to the right.Principal axis may include a pitch axis. As used in this disclosure a“pitch axis” is an axis that is directed towards the right laterallyextending wing of the aircraft. For example, and without limitation, apositive pitching motion may include adjusting and/or shifting the noseof aircraft 500 upwards. Principal axis may include a roll axis. As usedin this disclosure a “roll axis” is an axis that is directedlongitudinally towards the nose of the aircraft, parallel to thefuselage. For example, and without limitation, a positive rolling motionmay include lifting the left and lowering the right wing concurrently.

Still referring to FIG. 5 , pilot control 512 may be configured tomodify a variable pitch angle. For example, and without limitation,pilot control 512 may adjust one or more angles of attack of apropeller. As used in this disclosure an “angle of attack” is an anglebetween the chord of the propeller and the relative wind. For example,and without limitation angle of attack may include a propeller bladeangled 3.2°. In an embodiment, pilot control 512 may modify the variablepitch angle from a first angle of 2.71° to a second angle of 3.82°.Additionally or alternatively, pilot control 512 may be configured totranslate a pilot desired torque for flight component 508. For example,and without limitation, pilot control 512 may translate that a pilot'sdesired torque for a propeller be 160 lb. ft. of torque. As a furthernon-limiting example, pilot control 512 may introduce a pilot's desiredtorque for a propulsor to be 290 lb. ft. of torque. Additionaldisclosure related to pilot control 512 may be found in U.S. patentapplication Ser. Nos. 17/001,845 and 16/929,206 both of which areentitled “A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT” byC. Spiegel et al., which are incorporated in their entirety herein byreference.

Still referring to FIG. 5 , aircraft 500 may include a loading system. Aloading system may include a system configured to load an aircraft ofeither cargo or personnel. For instance, some exemplary loading systemsmay include a swing nose, which is configured to swing the nose ofaircraft 500 of the way thereby allowing direct access to a cargo baylocated behind the nose. A notable exemplary swing nose aircraft isBoeing 747. Additional disclosure related to loading systems can befound in U.S. patent application Ser. No. 17/137,594 entitled “SYSTEMAND METHOD FOR LOADING AND SECURING PAYLOAD IN AN AIRCRAFT” by R.Griffin et al., entirety of which in incorporated herein by reference.

Still referring to FIG. 5 , aircraft 500 may include a sensor 516.Sensor 516 may include any sensor or noise monitoring circuit describedin this disclosure, for instance in reference to FIGS. 1-4 . Sensor 516may be configured to sense a characteristic of pilot control 512. Sensormay be a device, module, and/or subsystem, utilizing any hardware,software, and/or any combination thereof to sense a characteristicand/or changes thereof, in an instant environment, for instance withoutlimitation a pilot control 512, which the sensor is proximal to orotherwise in a sensed communication with, and transmit informationassociated with the characteristic, for instance without limitationdigitized data. Sensor 516 may be mechanically and/or communicativelycoupled to aircraft 500, including, for instance, to at least a pilotcontrol 512. Sensor 516 may be configured to sense a characteristicassociated with at least a pilot control 512. An environmental sensormay include without limitation one or more sensors used to detectambient temperature, barometric pressure, and/or air velocity, one ormore motion sensors which may include without limitation gyroscopes,accelerometers, inertial measurement unit (IMU), and/or magneticsensors, one or more humidity sensors, one or more oxygen sensors, orthe like. Additionally or alternatively, sensor 516 may include at leasta geospatial sensor. Sensor 516 may be located inside an aircraft;and/or be included in and/or attached to at least a portion of theaircraft. Sensor may include one or more proximity sensors, displacementsensors, vibration sensors, and the like thereof. Sensor may be used tomonitor the status of aircraft 500 for both critical and non-criticalfunctions. Sensor may be incorporated into vehicle or aircraft or beremote.

Still referring to FIG. 5 , in some embodiments, sensor 516 may beconfigured to sense a characteristic associated with any pilot controldescribed in this disclosure. Non-limiting examples of a sensor 516 mayinclude an inertial measurement unit (IMU), an accelerometer, agyroscope, a proximity sensor, a pressure sensor, a light sensor, apitot tube, an air speed sensor, a position sensor, a speed sensor, aswitch, a thermometer, a strain gauge, an acoustic sensor, and anelectrical sensor. In some cases, sensor 516 may sense a characteristicas an analog measurement, for instance, yielding a continuously variableelectrical potential indicative of the sensed characteristic. In thesecases, sensor 516 may additionally comprise an analog to digitalconverter (ADC) as well as any additionally circuitry, such as withoutlimitation a Whetstone bridge, an amplifier, a filter, and the like. Forinstance, in some cases, sensor 516 may comprise a strain gageconfigured to determine loading of one or flight components, forinstance landing gear. Strain gage may be included within a circuitcomprising a Whetstone bridge, an amplified, and a bandpass filter toprovide an analog strain measurement signal having a high signal tonoise ratio, which characterizes strain on a landing gear member. An ADCmay then digitize analog signal produces a digital signal that can thenbe transmitted other systems within aircraft 500, for instance withoutlimitation a computing system, a pilot display, and a memory component.Alternatively or additionally, sensor 516 may sense a characteristic ofa pilot control 512 digitally. For instance in some embodiments, sensor516 may sense a characteristic through a digital means or digitize asensed signal natively. In some cases, for example, sensor 516 mayinclude a rotational encoder and be configured to sense a rotationalposition of a pilot control; in this case, the rotational encoderdigitally may sense rotational “clicks” by any known method, such aswithout limitation magnetically, optically, and the like.

Still referring to FIG. 5 , electric aircraft 500 may include at least amotor 524, which may be mounted on a structural feature of the aircraft.Design of motor 524 may enable it to be installed external to structuralmember (such as a boom, nacelle, or fuselage) for easy maintenanceaccess and to minimize accessibility requirements for the structure.;this may improve structural efficiency by requiring fewer large holes inthe mounting area. In some embodiments, motor 524 may include two mainholes in top and bottom of mounting area to access bearing cartridge.Further, a structural feature may include a component of electricaircraft 500. For example, and without limitation structural feature maybe any portion of a vehicle incorporating motor 524, including anyvehicle as described in this disclosure. As a further non-limitingexample, a structural feature may include without limitation a wing, aspar, an outrigger, a fuselage, or any portion thereof; persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of many possible features that may function as at least astructural feature. At least a structural feature may be constructed ofany suitable material or combination of materials, including withoutlimitation metal such as aluminum, titanium, steel, or the like, polymermaterials or composites, fiberglass, carbon fiber, wood, or any othersuitable material. As a non-limiting example, at least a structuralfeature may be constructed from additively manufactured polymer materialwith a carbon fiber exterior; aluminum parts or other elements may beenclosed for structural strength, or for purposes of supporting, forinstance, vibration, torque or shear stresses imposed by at leastpropulsor 508. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various materials, combinations ofmaterials, and/or constructions techniques.

Still referring to FIG. 5 , electric aircraft 500 may include a verticaltakeoff and landing aircraft (eVTOL). As used herein, a verticaltake-off and landing (eVTOL) aircraft is one that can hover, take off,and land vertically. An eVTOL, as used herein, is an electricallypowered aircraft typically using an energy source, of a plurality ofenergy sources to power the aircraft. In order to optimize the power andenergy necessary to propel the aircraft. eVTOL may be capable ofrotor-based cruising flight, rotor-based takeoff, rotor-based landing,fixed-wing cruising flight, airplane-style takeoff, airplane-stylelanding, and/or any combination thereof. Rotor-based flight, asdescribed herein, is where the aircraft generated lift and propulsion byway of one or more powered rotors coupled with an engine, such as a“quad copter,” multi-rotor helicopter, or other vehicle that maintainsits lift primarily using downward thrusting propulsors. Fixed-wingflight, as described herein, is where the aircraft is capable of flightusing wings and/or foils that generate life caused by the aircraft'sforward airspeed and the shape of the wings and/or foils, such asairplane-style flight.

With continued reference to FIG. 5 , a number of aerodynamic forces mayact upon the electric aircraft 500 during flight. Forces acting onelectric aircraft 500 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 500 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 500 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 500 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 500 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 500 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 500 downward due to the force of gravity. Anadditional force acting on electric aircraft 500 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor 508 of the electric aircraft.Lift generated by the airfoil may depend on speed of airflow, density ofair, total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 500 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of electric aircraft 500,including without limitation propulsors and/or propulsion assemblies. Inan embodiment, motor 524 may eliminate need for many external structuralfeatures that otherwise might be needed to join one component to anothercomponent. Motor 524 may also increase energy efficiency by enabling alower physical propulsor profile, reducing drag and/or wind resistance.This may also increase durability by lessening the extent to which dragand/or wind resistance add to forces acting on electric aircraft 500and/or propulsors.

Now referring to FIG. 6 , an exemplary embodiment 600 of a flightcontroller 604 is illustrated. As used in this disclosure a “flightcontroller” is a computing device of a plurality of computing devicesdedicated to data storage, security, distribution of traffic for loadbalancing, and flight instruction. Flight controller 604 may includeand/or communicate with any computing device as described in thisdisclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Further, flight controller 604may include a single computing device operating independently, or mayinclude two or more computing device operating in concert, in parallel,sequentially or the like; two or more computing devices may be includedtogether in a single computing device or in two or more computingdevices. In embodiments, flight controller 604 may be installed in anaircraft, may control the aircraft remotely, and/or may include anelement installed in the aircraft and a remote element in communicationtherewith.

In an embodiment, and still referring to FIG. 6 , flight controller 604may include a signal transformation component 608. As used in thisdisclosure a “signal transformation component” is a component thattransforms and/or converts a first signal to a second signal, wherein asignal may include one or more digital and/or analog signals. Forexample, and without limitation, signal transformation component 608 maybe configured to perform one or more operations such as preprocessing,lexical analysis, parsing, semantic analysis, and the like thereof. Inan embodiment, and without limitation, signal transformation component608 may include one or more analog-to-digital convertors that transforma first signal of an analog signal to a second signal of a digitalsignal. For example, and without limitation, an analog-to-digitalconverter may convert an analog input signal to a 10-bit binary digitalrepresentation of that signal. In another embodiment, signaltransformation component 608 may include transforming one or morelow-level languages such as, but not limited to, machine languagesand/or assembly languages. For example, and without limitation, signaltransformation component 608 may include transforming a binary languagesignal to an assembly language signal. In an embodiment, and withoutlimitation, signal transformation component 608 may include transformingone or more high-level languages and/or formal languages such as but notlimited to alphabets, strings, and/or languages. For example, andwithout limitation, high-level languages may include one or more systemlanguages, scripting languages, domain-specific languages, visuallanguages, esoteric languages, and the like thereof. As a furthernon-limiting example, high-level languages may include one or morealgebraic formula languages, business data languages, string and listlanguages, object-oriented languages, and the like thereof.

Still referring to FIG. 6 , signal transformation component 608 may beconfigured to optimize an intermediate representation 612. As used inthis disclosure an “intermediate representation” is a data structureand/or code that represents the input signal. Signal transformationcomponent 608 may optimize intermediate representation as a function ofa data-flow analysis, dependence analysis, alias analysis, pointeranalysis, escape analysis, and the like thereof. In an embodiment, andwithout limitation, signal transformation component 608 may optimizeintermediate representation 612 as a function of one or more inlineexpansions, dead code eliminations, constant propagation, looptransformations, and/or automatic parallelization functions. In anotherembodiment, signal transformation component 608 may optimizeintermediate representation as a function of a machine dependentoptimization such as a peephole optimization, wherein a peepholeoptimization may rewrite short sequences of code into more efficientsequences of code. Signal transformation component 608 may optimizeintermediate representation to generate an output language, wherein an“output language,” as used herein, is the native machine language offlight controller 604. For example, and without limitation, nativemachine language may include one or more binary and/or numericallanguages.

In an embodiment, and without limitation, signal transformationcomponent 608 may include transform one or more inputs and outputs as afunction of an error correction code. An error correction code, alsoknown as error correcting code (ECC), is an encoding of a message or lotof data using redundant information, permitting recovery of corrupteddata. An ECC may include a block code, in which information is encodedon fixed-size packets and/or blocks of data elements such as symbols ofpredetermined size, bits, or the like. Reed-Solomon coding, in whichmessage symbols within a symbol set having q symbols are encoded ascoefficients of a polynomial of degree less than or equal to a naturalnumber k, over a finite field F with q elements; strings so encoded havea minimum hamming distance of k+1, and permit correction of (q−k−1)/2erroneous symbols. Block code may alternatively or additionally beimplemented using Golay coding, also known as binary Golay coding,Bose-Chaudhuri, Hocquenghuem (BCH) coding, multidimensional parity-checkcoding, and/or Hamming codes. An ECC may alternatively or additionallybe based on a convolutional code.

In an embodiment, and still referring to FIG. 6 , flight controller 604may include a reconfigurable hardware platform 616. A “reconfigurablehardware platform,” as used herein, is a component and/or unit ofhardware that may be reprogrammed, such that, for instance, a data pathbetween elements such as logic gates or other digital circuit elementsmay be modified to change an algorithm, state, logical sequence, or thelike of the component and/or unit. This may be accomplished with suchflexible high-speed computing fabrics as field-programmable gate arrays(FPGAs), which may include a grid of interconnected logic gates,connections between which may be severed and/or restored to program inmodified logic. Reconfigurable hardware platform 616 may be reconfiguredto enact any algorithm and/or algorithm selection process received fromanother computing device and/or created using machine-learningprocesses.

Still referring to FIG. 6 , reconfigurable hardware platform 616 mayinclude a logic component 620. As used in this disclosure a “logiccomponent” is a component that executes instructions on output language.For example, and without limitation, logic component may perform basicarithmetic, logic, controlling, input/output operations, and the likethereof. Logic component 620 may include any suitable processor, such aswithout limitation a component incorporating logical circuitry forperforming arithmetic and logical operations, such as an arithmetic andlogic unit (ALU), which may be regulated with a state machine anddirected by operational inputs from memory and/or sensors; logiccomponent 620 may be organized according to Von Neumann and/or Harvardarchitecture as a non-limiting example. Logic component 620 may include,incorporate, and/or be incorporated in, without limitation, amicrocontroller, microprocessor, digital signal processor (DSP), FieldProgrammable Gate Array (FPGA), Complex Programmable Logic Device(CPLD), Graphical Processing Unit (GPU), general purpose GPU, TensorProcessing Unit (TPU), analog or mixed signal processor, TrustedPlatform Module (TPM), a floating point unit (FPU), and/or system on achip (SoC). In an embodiment, logic component 620 may include one ormore integrated circuit microprocessors, which may contain one or morecentral processing units, central processors, and/or main processors, ona single metal-oxide-semiconductor chip. Logic component 620 may beconfigured to execute a sequence of stored instructions to be performedon the output language and/or intermediate representation 612. Logiccomponent 620 may be configured to fetch and/or retrieve the instructionfrom a memory cache, wherein a “memory cache,” as used in thisdisclosure, is a stored instruction set on flight controller 604. Logiccomponent 620 may be configured to decode the instruction retrieved fromthe memory cache to opcodes and/or operands. Logic component 620 may beconfigured to execute the instruction on intermediate representation 612and/or output language. For example, and without limitation, logiccomponent 620 may be configured to execute an addition operation onintermediate representation 612 and/or output language.

In an embodiment, and without limitation, logic component 620 may beconfigured to calculate a flight element 624. As used in this disclosurea “flight element” is an element of datum denoting a relative status ofaircraft. For example, and without limitation, flight element 624 maydenote one or more torques, thrusts, airspeed velocities, forces,altitudes, groundspeed velocities, directions during flight, directionsfacing, forces, orientations, and the like thereof. For example, andwithout limitation, flight element 624 may denote that aircraft iscruising at an altitude and/or with a sufficient magnitude of forwardthrust. As a further non-limiting example, flight status may denote thatis building thrust and/or groundspeed velocity in preparation for atakeoff. As a further non-limiting example, flight element 624 maydenote that aircraft is following a flight path accurately and/orsufficiently.

Still referring to FIG. 6 , flight controller 604 may include a chipsetcomponent 628. As used in this disclosure a “chipset component” is acomponent that manages data flow. In an embodiment, and withoutlimitation, chipset component 628 may include a northbridge data flowpath, wherein the northbridge dataflow path may manage data flow fromlogic component 620 to a high-speed device and/or component, such as aRAM, graphics controller, and the like thereof. In another embodiment,and without limitation, chipset component 628 may include a southbridgedata flow path, wherein the southbridge dataflow path may manage dataflow from logic component 620 to lower-speed peripheral buses, such as aperipheral component interconnect (PCI), industry standard architecture(ICA), and the like thereof. In an embodiment, and without limitation,southbridge data flow path may include managing data flow betweenperipheral connections such as ethernet, USB, audio devices, and thelike thereof. Additionally or alternatively, chipset component 628 maymanage data flow between logic component 620, memory cache, and a flightcomponent 632. As used in this disclosure a “flight component” is aportion of an aircraft that can be moved or adjusted to affect one ormore flight elements. For example, flight component 632 may include acomponent used to affect the aircrafts' roll and pitch which maycomprise one or more ailerons. As a further example, flight component632 may include a rudder to control yaw of an aircraft. In anembodiment, chipset component 628 may be configured to communicate witha plurality of flight components as a function of flight element 624.For example, and without limitation, chipset component 628 may transmitto an aircraft rotor to reduce torque of a first lift propulsor andincrease the forward thrust produced by a pusher component to perform aflight maneuver.

In an embodiment, and still referring to FIG. 6 , flight controller 604may be configured generate an autonomous function. As used in thisdisclosure an “autonomous function” is a mode and/or function of flightcontroller 604 that controls aircraft automatically. For example, andwithout limitation, autonomous function may perform one or more aircraftmaneuvers, take offs, landings, altitude adjustments, flight levelingadjustments, turns, climbs, and/or descents. As a further non-limitingexample, autonomous function may adjust one or more airspeed velocities,thrusts, torques, and/or groundspeed velocities. As a furthernon-limiting example, autonomous function may perform one or more flightpath corrections and/or flight path modifications as a function offlight element 624. In an embodiment, autonomous function may includeone or more modes of autonomy such as, but not limited to, autonomousmode, semi-autonomous mode, and/or non-autonomous mode. As used in thisdisclosure “autonomous mode” is a mode that automatically adjusts and/orcontrols aircraft and/or the maneuvers of aircraft in its entirety. Forexample, autonomous mode may denote that flight controller 604 willadjust the aircraft. As used in this disclosure a “semi-autonomous mode”is a mode that automatically adjusts and/or controls a portion and/orsection of aircraft. For example, and without limitation,semi-autonomous mode may denote that a pilot will control thepropulsors, wherein flight controller 604 will control the aileronsand/or rudders. As used in this disclosure “non-autonomous mode” is amode that denotes a pilot will control aircraft and/or maneuvers ofaircraft in its entirety.

In an embodiment, and still referring to FIG. 6 , flight controller 604may generate autonomous function as a function of an autonomousmachine-learning model. As used in this disclosure an “autonomousmachine-learning model” is a machine-learning model to produce anautonomous function output given flight element 624 and a pilot signal636 as inputs; this is in contrast to a non-machine learning softwareprogram where the commands to be executed are determined in advance by auser and written in a programming language. As used in this disclosure a“pilot signal” is an element of datum representing one or more functionsa pilot is controlling and/or adjusting. For example, pilot signal 636may denote that a pilot is controlling and/or maneuvering ailerons,wherein the pilot is not in control of the rudders and/or propulsors. Inan embodiment, pilot signal 636 may include an implicit signal and/or anexplicit signal. For example, and without limitation, pilot signal 636may include an explicit signal, wherein the pilot explicitly statesthere is a lack of control and/or desire for autonomous function. As afurther non-limiting example, pilot signal 636 may include an explicitsignal directing flight controller 604 to control and/or maintain aportion of aircraft, a portion of the flight plan, the entire aircraft,and/or the entire flight plan. As a further non-limiting example, pilotsignal 636 may include an implicit signal, wherein flight controller 604detects a lack of control such as by a malfunction, torque alteration,flight path deviation, and the like thereof. In an embodiment, andwithout limitation, pilot signal 636 may include one or more explicitsignals to reduce torque, and/or one or more implicit signals thattorque may be reduced due to reduction of airspeed velocity. In anembodiment, and without limitation, pilot signal 636 may include one ormore local and/or global signals. For example, and without limitation,pilot signal 636 may include a local signal that is transmitted by apilot and/or crew member. As a further non-limiting example, pilotsignal 636 may include a global signal that is transmitted by airtraffic control and/or one or more remote users that are incommunication with the pilot of aircraft. In an embodiment, pilot signal636 may be received as a function of a tri-state bus and/or multiplexorthat denotes an explicit pilot signal should be transmitted prior to anyimplicit or global pilot signal.

Still referring to FIG. 6 , autonomous machine-learning model mayinclude one or more autonomous machine-learning processes such assupervised, unsupervised, or reinforcement machine-learning processesthat flight controller 604 and/or a remote device may or may not use inthe generation of autonomous function. As used in this disclosure“remote device” is an external device to flight controller 604.Additionally or alternatively, autonomous machine-learning model mayinclude one or more autonomous machine-learning processes that afield-programmable gate array (FPGA) may or may not use in thegeneration of autonomous function. Autonomous machine-learning processmay include, without limitation machine learning processes such assimple linear regression, multiple linear regression, polynomialregression, support vector regression, ridge regression, lassoregression, elasticnet regression, decision tree regression, randomforest regression, logistic regression, logistic classification,K-nearest neighbors, support vector machines, kernel support vectormachines, naive bayes, decision tree classification, random forestclassification, K-means clustering, hierarchical clustering,dimensionality reduction, principal component analysis, lineardiscriminant analysis, kernel principal component analysis, Q-learning,State Action Reward State Action (SARSA), Deep-Q network, Markovdecision processes, Deep Deterministic Policy Gradient (DDPG), or thelike thereof.

In an embodiment, and still referring to FIG. 6 , autonomous machinelearning model may be trained as a function of autonomous training data,wherein autonomous training data may correlate a flight element, pilotsignal, and/or simulation data to an autonomous function. For example,and without limitation, a flight element of an airspeed velocity, apilot signal of limited and/or no control of propulsors, and asimulation data of required airspeed velocity to reach the destinationmay result in an autonomous function that includes a semi-autonomousmode to increase thrust of the propulsors. Autonomous training data maybe received as a function of user-entered valuations of flight elements,pilot signals, simulation data, and/or autonomous functions. Flightcontroller 604 may receive autonomous training data by receivingcorrelations of flight element, pilot signal, and/or simulation data toan autonomous function that were previously received and/or determinedduring a previous iteration of generation of autonomous function.Autonomous training data may be received by one or more remote devicesand/or FPGAs that at least correlate a flight element, pilot signal,and/or simulation data to an autonomous function. Autonomous trainingdata may be received in the form of one or more user-enteredcorrelations of a flight element, pilot signal, and/or simulation datato an autonomous function.

Still referring to FIG. 6 , flight controller 604 may receive autonomousmachine-learning model from a remote device and/or FPGA that utilizesone or more autonomous machine learning processes, wherein a remotedevice and an FPGA is described above in detail. For example, andwithout limitation, a remote device may include a computing device,external device, processor, FPGA, microprocessor and the like thereof.Remote device and/or FPGA may perform the autonomous machine-learningprocess using autonomous training data to generate autonomous functionand transmit the output to flight controller 604. Remote device and/orFPGA may transmit a signal, bit, datum, or parameter to flightcontroller 604 that at least relates to autonomous function.Additionally or alternatively, the remote device and/or FPGA may providean updated machine-learning model. For example, and without limitation,an updated machine-learning model may be comprised of a firmware update,a software update, a autonomous machine-learning process correction, andthe like thereof. As a non-limiting example a software update mayincorporate a new simulation data that relates to a modified flightelement. Additionally or alternatively, the updated machine learningmodel may be transmitted to the remote device and/or FPGA, wherein theremote device and/or FPGA may replace the autonomous machine-learningmodel with the updated machine-learning model and generate theautonomous function as a function of the flight element, pilot signal,and/or simulation data using the updated machine-learning model. Theupdated machine-learning model may be transmitted by the remote deviceand/or FPGA and received by flight controller 604 as a software update,firmware update, or corrected autonomous machine-learning model. Forexample, and without limitation autonomous machine learning model mayutilize a neural net machine-learning process, wherein the updatedmachine-learning model may incorporate a gradient boostingmachine-learning process.

Still referring to FIG. 6 , flight controller 604 may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Further, flight controller may communicate withone or more additional devices as described below in further detail viaa network interface device. The network interface device may be utilizedfor commutatively connecting a flight controller to one or more of avariety of networks, and one or more devices. Examples of a networkinterface device include, but are not limited to, a network interfacecard (e.g., a mobile network interface card, a LAN card), a modem, andany combination thereof. Examples of a network include, but are notlimited to, a wide area network (e.g., the Internet, an enterprisenetwork), a local area network (e.g., a network associated with anoffice, a building, a campus or other relatively small geographicspace), a telephone network, a data network associated with atelephone/voice provider (e.g., a mobile communications provider dataand/or voice network), a direct connection between two computingdevices, and any combinations thereof. The network may include anynetwork topology and can may employ a wired and/or a wireless mode ofcommunication.

In an embodiment, and still referring to FIG. 6 , flight controller 604may include, but is not limited to, for example, a cluster of flightcontrollers in a first location and a second flight controller orcluster of flight controllers in a second location. Flight controller604 may include one or more flight controllers dedicated to datastorage, security, distribution of traffic for load balancing, and thelike. Flight controller 604 may be configured to distribute one or morecomputing tasks as described below across a plurality of flightcontrollers, which may operate in parallel, in series, redundantly, orin any other manner used for distribution of tasks or memory betweencomputing devices. For example, and without limitation, flightcontroller 604 may implement a control algorithm to distribute and/orcommand the plurality of flight controllers. As used in this disclosurea “control algorithm” is a finite sequence of well-defined computerimplementable instructions that may determine the flight component ofthe plurality of flight components to be adjusted. For example, andwithout limitation, control algorithm may include one or more algorithmsthat reduce and/or prevent aviation asymmetry. As a further non-limitingexample, control algorithms may include one or more models generated asa function of a software including, but not limited to Simulink byMathWorks, Natick, Mass., USA. In an embodiment, and without limitation,control algorithm may be configured to generate an auto-code, wherein an“auto-code,” is used herein, is a code and/or algorithm that isgenerated as a function of the one or more models and/or software's. Inanother embodiment, control algorithm may be configured to produce asegmented control algorithm. As used in this disclosure a “segmentedcontrol algorithm” is control algorithm that has been separated and/orparsed into discrete sections. For example, and without limitation,segmented control algorithm may parse control algorithm into two or moresegments, wherein each segment of control algorithm may be performed byone or more flight controllers operating on distinct flight components.

In an embodiment, and still referring to FIG. 6 , control algorithm maybe configured to determine a segmentation boundary as a function ofsegmented control algorithm. As used in this disclosure a “segmentationboundary” is a limit and/or delineation associated with the segments ofthe segmented control algorithm. For example, and without limitation,segmentation boundary may denote that a segment in the control algorithmhas a first starting section and/or a first ending section. As a furthernon-limiting example, segmentation boundary may include one or moreboundaries associated with an ability of flight component 632. In anembodiment, control algorithm may be configured to create an optimizedsignal communication as a function of segmentation boundary. Forexample, and without limitation, optimized signal communication mayinclude identifying the discrete timing required to transmit and/orreceive the one or more segmentation boundaries. In an embodiment, andwithout limitation, creating optimized signal communication furthercomprises separating a plurality of signal codes across the plurality offlight controllers. For example, and without limitation the plurality offlight controllers may include one or more formal networks, whereinformal networks transmit data along an authority chain and/or arelimited to task-related communications. As a further non-limitingexample, communication network may include informal networks, whereininformal networks transmit data in any direction. In an embodiment, andwithout limitation, the plurality of flight controllers may include achain path, wherein a “chain path,” as used herein, is a linearcommunication path comprising a hierarchy that data may flow through. Inan embodiment, and without limitation, the plurality of flightcontrollers may include an all-channel path, wherein an “all-channelpath,” as used herein, is a communication path that is not restricted toa particular direction. For example, and without limitation, data may betransmitted upward, downward, laterally, and the like thereof. In anembodiment, and without limitation, the plurality of flight controllersmay include one or more neural networks that assign a weighted value toa transmitted datum. For example, and without limitation, a weightedvalue may be assigned as a function of one or more signals denoting thata flight component is malfunctioning and/or in a failure state.

Still referring to FIG. 6 , the plurality of flight controllers mayinclude a master bus controller. As used in this disclosure a “masterbus controller” is one or more devices and/or components that areconnected to a bus to initiate a direct memory access transaction,wherein a bus is one or more terminals in a bus architecture. Master buscontroller may communicate using synchronous and/or asynchronous buscontrol protocols. In an embodiment, master bus controller may includeflight controller 604. In another embodiment, master bus controller mayinclude one or more universal asynchronous receiver-transmitters (UART).For example, and without limitation, master bus controller may includeone or more bus architectures that allow a bus to initiate a directmemory access transaction from one or more buses in the busarchitectures. As a further non-limiting example, master bus controllermay include one or more peripheral devices and/or components tocommunicate with another peripheral device and/or component and/or themaster bus controller. In an embodiment, master bus controller may beconfigured to perform bus arbitration. As used in this disclosure “busarbitration” is method and/or scheme to prevent multiple buses fromattempting to communicate with and/or connect to master bus controller.For example and without limitation, bus arbitration may include one ormore schemes such as a small computer interface system, wherein a smallcomputer interface system is a set of standards for physical connectingand transferring data between peripheral devices and master buscontroller by defining commands, protocols, electrical, optical, and/orlogical interfaces. In an embodiment, master bus controller may receiveintermediate representation 612 and/or output language from logiccomponent 620, wherein output language may include one or moreanalog-to-digital conversions, low bit rate transmissions, messageencryptions, digital signals, binary signals, logic signals, analogsignals, and the like thereof described above in detail.

Still referring to FIG. 6 , master bus controller may communicate with aslave bus. As used in this disclosure a “slave bus” is one or moreperipheral devices and/or components that initiate a bus transfer. Forexample, and without limitation, slave bus may receive one or morecontrols and/or asymmetric communications from master bus controller,wherein slave bus transfers data stored to master bus controller. In anembodiment, and without limitation, slave bus may include one or moreinternal buses, such as but not limited to a/an internal data bus,memory bus, system bus, front-side bus, and the like thereof. In anotherembodiment, and without limitation, slave bus may include one or moreexternal buses such as external flight controllers, external computers,remote devices, printers, aircraft computer systems, flight controlsystems, and the like thereof.

In an embodiment, and still referring to FIG. 6 , control algorithm mayoptimize signal communication as a function of determining one or morediscrete timings. For example, and without limitation master buscontroller may synchronize timing of the segmented control algorithm byinjecting high priority timing signals on a bus of the master buscontrol. As used in this disclosure a “high priority timing signal” isinformation denoting that the information is important. For example, andwithout limitation, high priority timing signal may denote that asection of control algorithm is of high priority and should be analyzedand/or transmitted prior to any other sections being analyzed and/ortransmitted. In an embodiment, high priority timing signal may includeone or more priority packets. As used in this disclosure a “prioritypacket” is a formatted unit of data that is communicated between theplurality of flight controllers. For example, and without limitation,priority packet may denote that a section of control algorithm should beused and/or is of greater priority than other sections.

Still referring to FIG. 6 , flight controller 604 may also beimplemented using a “shared nothing” architecture in which data iscached at the worker, in an embodiment, this may enable scalability ofaircraft and/or computing device. Flight controller 604 may include adistributer flight controller. As used in this disclosure a “distributerflight controller” is a component that adjusts and/or controls aplurality of flight components as a function of a plurality of flightcontrollers. For example, distributer flight controller may include aflight controller that communicates with a plurality of additionalflight controllers and/or clusters of flight controllers. In anembodiment, distributed flight control may include one or more neuralnetworks. For example, neural network also known as an artificial neuralnetwork, is a network of “nodes,” or data structures having one or moreinputs, one or more outputs, and a function determining outputs based oninputs. Such nodes may be organized in a network, such as withoutlimitation a convolutional neural network, including an input layer ofnodes, one or more intermediate layers, and an output layer of nodes.Connections between nodes may be created via the process of “training”the network, in which elements from a training dataset are applied tothe input nodes, a suitable training algorithm (such asLevenberg-Marquardt, conjugate gradient, simulated annealing, or otheralgorithms) is then used to adjust the connections and weights betweennodes in adjacent layers of the neural network to produce the desiredvalues at the output nodes. This process is sometimes referred to asdeep learning.

Still referring to FIG. 6 , a node may include, without limitation aplurality of inputs x_(i) that may receive numerical values from inputsto a neural network containing the node and/or from other nodes. Nodemay perform a weighted sum of inputs using weights w_(i) that aremultiplied by respective inputs x_(i). Additionally or alternatively, abias b may be added to the weighted sum of the inputs such that anoffset is added to each unit in the neural network layer that isindependent of the input to the layer. The weighted sum may then beinput into a function φ, which may generate one or more outputs y.Weight w_(i) applied to an input x_(i) may indicate whether the input is“excitatory,” indicating that it has strong influence on the one or moreoutputs y, for instance by the corresponding weight having a largenumerical value, and/or a “inhibitory,” indicating it has a weak effectinfluence on the one more inputs y, for instance by the correspondingweight having a small numerical value. The values of weights w_(i) maybe determined by training a neural network using training data, whichmay be performed using any suitable process as described above. In anembodiment, and without limitation, a neural network may receivesemantic units as inputs and output vectors representing such semanticunits according to weights w_(i) that are derived using machine-learningprocesses as described in this disclosure.

Still referring to FIG. 6 , flight controller may include asub-controller 640. As used in this disclosure a “sub-controller” is acontroller and/or component that is part of a distributed controller asdescribed above; for instance, flight controller 604 may be and/orinclude a distributed flight controller made up of one or moresub-controllers. For example, and without limitation, sub-controller 640may include any controllers and/or components thereof that are similarto distributed flight controller and/or flight controller as describedabove. Sub-controller 640 may include any component of any flightcontroller as described above. Sub-controller 640 may be implemented inany manner suitable for implementation of a flight controller asdescribed above. As a further non-limiting example, sub-controller 640may include one or more processors, logic components and/or computingdevices capable of receiving, processing, and/or transmitting dataacross the distributed flight controller as described above. As afurther non-limiting example, sub-controller 640 may include acontroller that receives a signal from a first flight controller and/orfirst distributed flight controller component and transmits the signalto a plurality of additional sub-controllers and/or flight components.

Still referring to FIG. 6 , flight controller may include aco-controller 644. As used in this disclosure a “co-controller” is acontroller and/or component that joins flight controller 604 ascomponents and/or nodes of a distributer flight controller as describedabove. For example, and without limitation, co-controller 644 mayinclude one or more controllers and/or components that are similar toflight controller 604. As a further non-limiting example, co-controller644 may include any controller and/or component that joins flightcontroller 604 to distributer flight controller. As a furthernon-limiting example, co-controller 644 may include one or moreprocessors, logic components and/or computing devices capable ofreceiving, processing, and/or transmitting data to and/or from flightcontroller 604 to distributed flight control system. Co-controller 644may include any component of any flight controller as described above.Co-controller 644 may be implemented in any manner suitable forimplementation of a flight controller as described above.

In an embodiment, and with continued reference to FIG. 6 , flightcontroller 604 may be designed and/or configured to perform any method,method step, or sequence of method steps in any embodiment described inthis disclosure, in any order and with any degree of repetition. Forinstance, flight controller 604 may be configured to perform a singlestep or sequence repeatedly until a desired or commanded outcome isachieved; repetition of a step or a sequence of steps may be performediteratively and/or recursively using outputs of previous repetitions asinputs to subsequent repetitions, aggregating inputs and/or outputs ofrepetitions to produce an aggregate result, reduction or decrement ofone or more variables such as global variables, and/or division of alarger processing task into a set of iteratively addressed smallerprocessing tasks. Flight controller may perform any step or sequence ofsteps as described in this disclosure in parallel, such assimultaneously and/or substantially simultaneously performing a step twoor more times using two or more parallel threads, processor cores, orthe like; division of tasks between parallel threads and/or processesmay be performed according to any protocol suitable for division oftasks between iterations. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways in whichsteps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

Referring now to FIG. 7 , an exemplary embodiment of a machine-learningmodule 700 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 704 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 708 given data provided as inputs 712;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language.

Still referring to FIG. 7 , “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 704 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 704 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 704 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 704 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 704 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 704 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data704 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 7 ,training data 704 may include one or more elements that are notcategorized; that is, training data 704 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 704 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 704 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 704 used by machine-learning module 700 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample flight elements and/or pilot signals may be inputs, wherein anoutput may be an autonomous function.

Further referring to FIG. 7 , training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 716. Training data classifier 716 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 700 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 704. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 416 may classify elements of training data tosub-categories of flight elements such as torques, forces, thrusts,directions, and the like thereof.

Still referring to FIG. 7 , machine-learning module 700 may beconfigured to perform a lazy-learning process 720 and/or protocol, whichmay alternatively be referred to as a “lazy loading” or“call-when-needed” process and/or protocol, may be a process wherebymachine learning is conducted upon receipt of an input to be convertedto an output, by combining the input and training set to derive thealgorithm to be used to produce the output on demand. For instance, aninitial set of simulations may be performed to cover an initialheuristic and/or “first guess” at an output and/or relationship. As anon-limiting example, an initial heuristic may include a ranking ofassociations between inputs and elements of training data 704. Heuristicmay include selecting some number of highest-ranking associations and/ortraining data 704 elements. Lazy learning may implement any suitablelazy learning algorithm, including without limitation a K-nearestneighbors algorithm, a lazy naïve Bayes algorithm, or the like; personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various lazy-learning algorithms that may be applied togenerate outputs as described in this disclosure, including withoutlimitation lazy learning applications of machine-learning algorithms asdescribed in further detail below.

Alternatively or additionally, and with continued reference to FIG. 7 ,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 724. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 724 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 724 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 704set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 7 , machine-learning algorithms may include atleast a supervised machine-learning process 728. At least a supervisedmachine-learning process 728, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude flight elements and/or pilot signals as described above asinputs, autonomous functions as outputs, and a scoring functionrepresenting a desired form of relationship to be detected betweeninputs and outputs; scoring function may, for instance, seek to maximizethe probability that a given input and/or combination of elements inputsis associated with a given output to minimize the probability that agiven input is not associated with a given output. Scoring function maybe expressed as a risk function representing an “expected loss” of analgorithm relating inputs to outputs, where loss is computed as an errorfunction representing a degree to which a prediction generated by therelation is incorrect when compared to a given input-output pairprovided in training data 704. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of variouspossible variations of at least a supervised machine-learning process728 that may be used to determine relation between inputs and outputs.Supervised machine-learning processes may include classificationalgorithms as defined above.

Further referring to FIG. 7 , machine learning processes may include atleast an unsupervised machine-learning processes 732. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 7 , machine-learning module 700 may be designedand configured to create a machine-learning model 724 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 7 , machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naive Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 8 , an exemplary method 800 of electric propulsorfault detection is illustrated by way of flow diagram. At step 805,method 800 may include accepting, using at least a first inverter, adirect current. First inverter may include any inverter described inthis disclosure, for example with reference to FIGS. 1-7 . Directcurrent may include any direct current described in this disclosure, forexample with reference to FIGS. 1-7 .

With continued reference to FIG. 8 , at step 810, method 800 may includeproducing, using at least a first inverter, an alternating current.Alternating current may include any alternating current described inthis disclosure, for example with reference to FIGS. 1-7 .

With continued reference to FIG. 8 , at step 815, method 800 may includepowering, using alternating current, a first motor operatively connectedwith a first propulsor. First motor may include any motor described inthis disclosure, for example with reference to FIGS. 1-7 . Firstpropulsor may include any propulsor described in this disclosure, forexample with reference to FIGS. 1-7 .

With continued reference to FIG. 8 , at step 820, method 800 may includedetecting, using at least a noise monitoring circuit electricallyconnected with direct current, electromagnetic noise. Noise monitoringcircuit may include any noise monitoring circuit described in thisdisclosure, for example with reference to FIGS. 1-7 . Electromagneticnoise may include any electromagnetic noise described in thisdisclosure, for example with reference to FIGS. 1-7 . In someembodiments, at least a noise monitoring circuit may include anelectromagnetic interference (EMI) filter. In some versions, noisemonitoring circuit may include at least a noise detection signalinductively connected with EMI filter. In some versions, electromagneticnoise may include a common mode noise; and EMI filter may include acommon mode filter. In some versions, electromagnetic noise may includea differential mode noise; and EMI filter may include a differentialmode filter. In some embodiments, electromagnetic noise may beindicative of fault. In some cases, fault may include a motor fault. Insome cases, fault may include an inverter fault.

With continued reference to FIG. 8 , at step 825, method 800 may includedisengaging, using at least a noise monitoring circuit, at least aninverter as a function of electromagnetic noise.

Still referring to FIG. 8 , in some embodiments, first propulsor may bemounted to an electric aircraft; and method 800 may additionally includeproducing, using the first propulsor, lift. In some versions, method 800may additionally include powering, using a second inverter, a secondmotor operatively connected with a second propulsor and producing, usingthe second propulsor mounted to the electric aircraft, lift. In someversions, electromagnetic noise disturbs at least one of secondinverter, second motor, and second propulsor.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random-access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 9 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 900 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 900 includes a processor 904 and a memory908 that communicate with each other, and with other components, via abus 912. Bus 912 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 904 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 904 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 904 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating-pointunit (FPU), and/or system on a chip (SoC).

Memory 908 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 916 (BIOS), including basic routines that help totransfer information between elements within computer system 900, suchas during start-up, may be stored in memory 908. Memory 908 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 920 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 908 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 900 may also include a storage device 924. Examples of astorage device (e.g., storage device 924) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 924 may be connected to bus 912 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 924 (or one or morecomponents thereof) may be removably interfaced with computer system 900(e.g., via an external port connector (not shown)). Particularly,storage device 924 and an associated machine-readable medium 928 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 900. In one example, software 920 may reside, completelyor partially, within machine-readable medium 928. In another example,software 920 may reside, completely or partially, within processor 904.

Computer system 900 may also include an input device 932. In oneexample, a user of computer system 900 may enter commands and/or otherinformation into computer system 900 via input device 932. Examples ofan input device 932 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 932may be interfaced to bus 912 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 912, and any combinations thereof. Input device 932 mayinclude a touch screen interface that may be a part of or separate fromdisplay 936, discussed further below. Input device 932 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 900 via storage device 924 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 940. A network interfacedevice, such as network interface device 940, may be utilized forconnecting computer system 900 to one or more of a variety of networks,such as network 944, and one or more remote devices 948 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 944,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 920,etc.) may be communicated to and/or from computer system 900 via networkinterface device 940.

Computer system 900 may further include a video display adapter 952 forcommunicating a displayable image to a display device, such as displaydevice 936. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 952 and display device 936 may be utilized incombination with processor 904 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 900 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 912 via a peripheral interface 956. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. An electric propulsor fault detection systemcomprising: at least a first inverter configured to: accept a directcurrent; and power a first motor; a first propulsor, wherein the firstpropulsor is mounted to an electric aircraft and configured to producelift; the first motor operatively connected with the first propulsor;and at least a noise monitoring circuit electrically connected with thedirect current and configured to: detect noise having a frequency withina specified range; and disengage the at least an inverter as a functionof the detected noise.
 2. The system of claim 1, wherein the at least anoise monitoring circuit comprises an electromagnetic interference (EMI)filter.
 3. The system of claim 2, wherein the noise monitoring circuitcomprises at least a noise detection signal inductively connected withthe EMI filter.
 4. The system of claim 2, wherein the noise comprises acommon mode noise; and the EMI filter comprises a common mode filter. 5.The system of claim 2, wherein the noise comprises a differential modenoise; and the EMI filter comprises a differential mode filter.
 6. Thesystem of claim 1, wherein the noise is indicative of fault.
 7. Thesystem of claim 6, wherein the fault comprises a motor fault.
 8. Thesystem of claim 6, wherein the fault comprises an inverter fault.
 9. Thesystem of claim 1, wherein the specified range is limited by a cutofffrequency.
 10. The system of claim 1, further comprising: at least asecond inverter; a second motor powered by the second inverter; a secondpropulsor operatively connected to the second motor, mounted to theelectric aircraft, and configured to produce lift; and wherein the noisedisturbs at least one of the second inverter, the second motor, and thesecond propulsor.
 11. A method of electric propulsor fault detectioncomprising: accepting, using at least a first inverter, a directcurrent; powering, using the at least a first inverter, a first motoroperatively connected with a first propulsor, wherein the firstpropulsor is mounted to an electric aircraft and configured to producelift; detecting, using at least a noise monitoring circuit electricallyconnected with the direct current, noise having a frequency within aspecified range; and disengage, using the at least a noise monitoringcircuit, the at least an inverter as a function of the detected noise.12. The method of claim 11, wherein the at least a noise monitoringcircuit comprises an electromagnetic interference (EMI) filter.
 13. Themethod of claim 12, wherein the noise monitoring circuit comprises atleast a noise detection signal inductively connected with the EMIfilter.
 14. The method of claim 12, wherein the noise comprises a commonmode noise; and the EMI filter comprises a common mode filter.
 15. Themethod of claim 12, wherein the noise comprises a differential modenoise; and the EMI filter comprises a differential mode filter.
 16. Themethod of claim 11, wherein the noise is indicative of fault.
 17. Themethod of claim 16, wherein the fault comprises a motor fault.
 18. Themethod of claim 16, wherein the fault comprises an inverter fault. 19.The method of claim 11, wherein the specified range is limited by acutoff frequency.
 20. The method of claim 19, further comprising:powering, using a second inverter, a second motor operatively connectedwith a second propulsor; producing, using the second propulsor mountedto the electric aircraft, lift; and wherein the noise disturbs at leastone of the second inverter, the second motor, and the second propulsor.